Quantifying overheating risk in English schools: A spatially coherent climate risk assessment

Introduction Cardiac anatomy, including heart size, position and orientation within the torso, is known to impact ECG morphology. Understanding the relationship between cardiac anatomy and ECG morphology through their main axes has the potential to lead to more accurate and reliable ECG biomarkers. Nevertheless, there is no standardised method for the definition of the anatomical and electrical axes of the heart. Aims To evaluate definitions of anatomical and electrical axes of the heart based on a derived metric in an undiseased population in the UKBioBank. Methods The anatomical and electrical axes of 2189 healthy subjects were computed separately in five different ways (Figure 1). Cardiac bi-ventricular anatomy was automatically segmented from cardiac MRI. Anatomical axes were computed from different combinations of anatomical landmarks (valves and apex) and axis of inertia. ECGs were transformed into vectorcardiograms (VCGs) using the Kors transformation. A range of combinations of dipole magnitudes, weighted averages and loop shape were used to define the electrical axes of the QRS loop. The anatomical-electrical relationship was quantified with the cosine of the angle between each of the 25 pairs of anatomical and electrical axes definitions. Spatial consistency was assessed by the standard deviation of this cosine across the cohort, where a higher level of spatial consistency corresponds to a lower standard deviation. Results Spatial consistency between anatomical and electrical axes exhibited a large range of standard deviation values from 0.61 for the worst pair to 0.14 for the best pair of definitions. The pair with the highest spatial consistency was defined by the valvular plane centre and the apex (VPA) for the anatomical axis, and the direction of maximum QRS dipole magnitude (maxQRS) in the VCG for the electrical axis (Panel A and B in Figure 2). The anatomical-electrical relationship was not significantly correlated with BMI, age or gender despite the individual anatomical and electrical axes showing significant correlation (Frontal plane: anatomical axis was most impacted by BMI, R=0.53 & p<0.0001, and electrical axis by gender R=0.37 & p<0.0001). Conclusion In this healthy population, the most spatially consistent definition of anatomical and electrical axes is defined by VPA and maxQRS. This spatial consistency is not affected by BMI, age or gender, suggesting an intrinsic relationship between the pair of measures, and represents a potential strategy to permit correction for patient anatomic factors when interpreting ECGs.Figure 1.Axes Definition MethodsFigure 2.Results

In many virtual reality systems, user physical workspace is superposed with a particular area in a virtual environment. The spatial consistency between the real and virtual interactive space allows users to take advantage of physical workspace to walk and to interact intuitively with the real and virtual contents. To maintain such spatial consistency, application designers usually deactivate user virtual navigation capability. This limits user reachable virtual area, and segments the spatial consistency required sub-task from a continuous scenario mixing large scale navigation. In order to provide users with a continuous virtual experience, we introduce two switch techniques to help users to recover the spatial consistency in some predefined virtual areas with teleportation navigation: simple switch and improved switch. We conducted a user study with a box-opening task in a CAVE-like system to evaluate the performance and usability of these techniques under different conditions. The results highlight that assisting the user on switching back to a spatially consistent situation ensures entire workspace accessibility and decreases time and cognitive effort used to complete the sub-task. The simple switch results in less task completion time, less cognitive load, and is globally preferred by users. With additional visual feedback of user switch destination, the improved switch seems to provide the user with a better understanding of the resulting spatial configuration of the switch.

Relative spatial consistency - that is, the stable arrangement of objects in a 2D presentation - provides several benefits for interactive interfaces. Spatial consistency allows users to develop memory of object locations, reducing the time needed for visual search, and because spatial memory is long lasting and has a large capacity these performance benefits are enduring and scalable. This suggests that spatial consistency could be used as a fundamental principle for the design of interfaces. However, there are many display situations where the standard presentation is altered in some way: e.g., a window is moved to a new location, scaled, or rotated on a mobile or tabletop display. It is not known whether the benefits of spatial organization are robust to these common kinds of view transformation. To assess these effects, we tested user performance with a spatial interface that had been transformed in several ways, including different degrees of translation, rotation, scaling, and perspective change. We found that performance was not strongly affected by the changes, except in the case of large rotations. To demonstrate the value of spatial consistency over existing mechanisms for dealing with view changes, we compared user performance with a spatially-stable presentation (using scaling) with that of a 'reflowing' presentation (widely used in current interfaces). This study showed that spatial stability with scaling dramatically outperforms reflowing. This research provides new evidence of spatial consistency's value in interface design: it is robust to the view transformations that occur in typical environments, and it provides substantial performance advantages over traditional methods.

The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.

The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.

Alternative Measures of Physiological Stress in Nursery Pigs and Broiler Chickens

We are interested in deducing whether two users in a cellular system are at nearby physical locations from measuring similarity of their covariance matrices at a base station. This becomes challenging in multiple-input-multiple-output mmWave channels, as the semi-optical nature of mmWave radio propagation gives rise to non-Kronecker correlation. Hence, the estimated BS covariance matrix depends on the UE pilot beamformer, and moreover, on the direction of movement in the radio environment. A coordinated UE pilot transmission approach is needed to make measured covariances spatially consistent. We formulate the UE pilot beamformer selection problem as an optimization problem aiming to preserve the spatial consistency of a set of UEs moving in the same large-scale radio environment. We use the collinearity matrix distance to measure the similarity of the BS covariance matrices of UEs in the radio environment. Covariance matrix and instantaneous channel state based UE pilot beamformers with different ranks are considered. Simulations are used to evaluate the spatial consistency provided by coordinated uplink precoding methods. Depending on the expected signal-to-noise ratio, there is an optimal rank for the UE pilot transmission, which maximizes the similarity between covariances estimated from transmissions of different UEs in the same large-scale fading environment.

Quantifying uncertainty and sensitivity in climate risk assessments: Varying hazard, exposure and vulnerability modelling choices

Impact Forecasting, a catastrophe model development branch of Aon, develops catastrophe models for various countries and perils, including floods, windstorms, earthquakes, wildfires, hurricanes, and typhoons. These models are crucial for the insurance and reinsurance industry to estimate losses in terms of severity and frequency. To address the increasing demand for evaluating losses across multiple countries and perils, Impact Forecasting has started using large ensembles of global climate models (GCM) and regional climate models (RCM). These models serve as a common forcing input for catastrophe models related to atmospheric perils such as flooding (fluvial, pluvial, and coastal), summer storms, windstorms, and wildfires. The use of GCM/RCM as common forcing input offers two main advantages: Spatial Consistency: The data are spatially consistent at a global or continental scale, which helps in addressing the issue of cross-country correlations. Variability: The large number of available ensembles provides sufficient variability to build a representative stochastic catalogue of potential catastrophes. We will present several examples of this approach (Pan-European flood model, the Canadian flood and wildfire models), where common GCM/RCM inputs are used to provide a consistent view of losses across large regions and various perils. We will also show how we adress the issue of low resolution of GCM/RCM models using machine learning.

The accurate prediction of the Fire Weather Index (FWI) is vital for effective wildfire management and climate-resilient planning. Multisite fire hazard forecasts are crucial for resource allocation, early intervention in high-risk areas, and identifying potential “megafire” threats from multiple simultaneous fire spots. Therefore, it is very important to account for the spatial consistency of these forecasts. This study examines the performance of Convolutional Neural Networks (CNNs) as a Statistical Downscaling (SD) technique for predicting FWI in different locations in the Iberian Peninsula. We contrast CNNs with two conventional SD methods: Generalized Linear Models and analogs. Using daily observed FWI data as predictands and ERA-Interim fields as predictors under a cross-validation setup, we discover that the CNN-Multi-Site-Multi-Gaussian (CNN-MSMG) model outperforms in daily FWI forecasting. This model integrates the covariance structure of the predictands into the CNN design, producing spatially consistent FWI forecasts. Furthermore, CNN-MSMG shows desirable features for estimating fire hazard in the climate change scenario, such as strong spatial consistency of extreme events and the capacity to generalize to new climate situations. These findings have important implications for improving FWI forecast accuracy and strengthening wildfire risk evaluation under climate change.

In this paper, we propose a novel method that uses coordinate alignment and background pixel extraction to synthesize highly accurate and spatially consistent intermediate views from a pair of stereo images and disparity maps. In contrast to the traditional depth image-based rendering (DIBR) method, where useful background pixels are discarded in the warping process, the proposed method extracts these background pixels and uses them as candidates for an exemplar-based image in-painting technique (EBIIT) to synthesize realistic content in disocclusion regions. Our second contribution is a coordinate alignment algorithm that aligns disocclusion regions in each view together and simultaneously synthesizes disocclusion regions to enhance spatial consistency across all virtual views. The proposed method compares favorably in quantitative measures to those obtained by existing techniques and has superior potential for stereo-to-multiview conversion.

High-resolution weather scenarios generated for climate change impact studies from the output of climate models must be spatially consistent. Analog models (AMs) offer a high potential for the generation of such scenarios. For each prediction day, the scenario they provide is the weather observed for days in a historical archive that are analogous according to different predictors. When the same “analog date” is chosen for a prediction at several sites, spatial consistency is automatically satisfied. The optimal predictors and consequently the optimal analog dates, however, are expected to depend on the location for which the prediction is to be made. In the present work, the predictor (1000- and 500-hPa geopotential heights) domain of a benchmark AM is optimized for the probabilistic daily prediction of 8981 local precipitation “stations” over France. The corresponding 8981 locally domain-optimized AMs are used to explore the spatial transferability and similarity of the optimal analog dates obtained for different locations. Whereas the similarity is very low even when the locations are close, the spatial transferability of the optimal analog dates for a given location is high. When they are used for the prediction at all other locations, the loss of prediction performance is therefore very low over large spatial domains (up to 500 km). Spatial transferability is lower in the presence of high mountains. It also depends on the parameters of the AM (e.g., its archive length, predictors, and number of analog dates used for the prediction). In the present case, AMs with higher prediction skill exhibit lower transferability.

For a class of cell division processes in the Euclidean space ℝd, spatial consistency is investigated. This addresses the problem whether the distribution of the generated structures, restricted to a bounded setV, depends on the choice of a larger regionW⊃Vwhere the construction of the cell division process is performed. This can also be understood as the problem of boundary effects in the cell division procedure. It is known that the STIT tessellations are spatially consistent. In the present paper it is shown that, within a reasonable wide class of cell division processes, the STIT tessellations are the only ones that are consistent.

This paper consider a fundamental issue of pathloss modeling in urban environments, namely the spatial consistency of the model as the mobile station (MS) moves along a trajectory through street canyons. We show that the traditional model of power law pathloss plus lognormally distributed variations can provide misleading results that can have serious implications for system simulations. Rather, the pathloss coefficient has to be modeled as a random variable that changes from street to street, and is also a function of the street orientation. Variations of the channel gain, taken over the ensemble of the whole cell (or multiple cells) thus consist of the compound effect of these pathloss coefficient variations together with traditional shadowing variations along the trajectory of movement. Ray tracing results demonstrate that ignoring this effect can lead to a severe overestimation of shadowing standard deviation. While the effect is irrelevant for “drop-based” simulations, it can have critical impact on system simulations that require spatial consistency for large-scale movement, such as most mm-wave systems.

While hydrological models aim to represent the hydrological behaviour of catchments, many of them have been streamlined on the exclusive basis of streamflow simulation performance, i.e. among the possible parameter sets, the 'optimal' is the one which brings the best simulation of streamflow during the calibration period. However, we sometimes encounter 'optimal' sets which perform well in discharge simulation but yield unrealistic simulations of other fluxes (e.g. actual evaporation fluxes, inter-catchment groundwater fluxes). Previous studies tried to constrain the exploration of parameter space with measurements complementary to river discharge: this application of extra information aims to increase the physical realism of the model compared to discharge-only calibration. In this study, we carry out an original investigation to take advantage of the spatial patterns of the complementary data in order to drive the calibration towards a more spatially consistent solution.We propose here a feasibility test, to constrain the spatial consistency of fluxes of a semi-distributed GR model (GRSD). Our study area is the Somme catchment (6100 km2 ) with 17 internal gauging stations, each of them having more than 15 years of discharge measurement. As a first step, we use the long-term actual evaporation from Budyko-estimation as an extra constraint, which has been widely used for describing spatial patterns of climate. In the second step, we develop a criterion describing the spatial consistency between the pattern of measured and simulated fluxes. By constraining the model with extra information, the model is expected to yield a more consistent simulation of fluxes in comparison with the classical calibration practice. Moreover, we analysed the impact of this additional constraint on the spatial organisation of IGF over the catchment as both the other components in water balance analysis, actual evaporation and discharge, are constrained.